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The low-level brightness-contrast illusions constitute a special class within visual illusions. Speculations exist that these illusions may be processed through the filtering action of the retinal ganglion cells without necessitating much intervention from higher order processes of visual perception. Concept of the classical receptive field of the ganglion cell, derived from early physiological studies, prompted the idea that a Difference of Gaussian (DoG) model might explain the low-level illusions. In spite of its many successes, the DoG model fails to explain some of these illusions. It has been shown in this paper that it is possible to simulate those illusions with a model that takes into cognizance the role of the extended classical receptive field.

The extended classical receptive field (ECRF) of retinal ganglion cells has been modelled as a combination of three zero-mean Gaussians at three different scales that has been shown to be equivalent to a Biharmonic or Bi-Laplacian of Gaussian filter. It has also been shown that the ECRF can be approximated by a combination of Laplacian of Gaussian (LoG) and the Dirac-delta function. Zero-crossings detected with this operator are more informative than those detected by the traditional filters like LoG or Difference of Gaussians (DoG) that had been devised using the classical receptive field of the ganglion cells. We have also explained that such an additional information processing is not in contradiction with the recent experimental findings on the physiology of retinal ganglion cells.

Traditionally the intensity discontinuities in an image are detected as zero-crossings of the second derivative with the help of a Laplacian of Gaussian (LOG) operator that models the receptive field of retinal Ganglion cells. Such zero-crossings supposedly form a raw primal sketch edge map of the external world in the primary visual cortex of the brain. Based on a new operator which is a linear combination of the LOG and a Dirac-delta function that models the extra-classical receptive field of the ganglion cells, we find that zero-crossing points thus generated, store in presence of noise, apart from the edge information, the shading information of the image in the form of density variation of these points. We have also shown that an optimal image contrast produces best mapping of the shading information to such zero-crossing density variation for a given amount of noise contamination. Furthermore, we have observed that an optimal amount of noise contamination reproduces the minimum optimal contrast and hence gives rise to the best representation of the original image. We show that this phenomenon is similar in nature to that of stochastic resonance phenomenon observed in psychophysical experiments.

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